When it is discovered that an individual has a disease, such as cancer, generally, there may be a mass of infected tissue that is desired to be observed to determine the extent of the disease and how any proposed treatment may affect the diseased tissue. Moreover, an individual may have some type of abnormality in an internal organ that also is desired to be observed in order to determine the proper type of treatment that is needed.
There now exists a number of systems that may be used to observe cancerous masses or abnormal organs. For example, X-ray, magnetic resonance imaging ("MRI"), computed tomography ("CT"), or other similar types of systems non-invasively provide the needed observations and produce an image of an area to be observed. If an X-ray (plane radiograph) is used, the two-dimensional image that is produced is a two-dimensional view of a projection of a three-dimensional volume of tissue. On the other hand, if MRI or CT systems are used, each two-dimensional image that is produced is a cross-sectional slice of a three-dimensional volume.
To view the infected tissue mass or abnormal organ, a series of two-dimensional images of sections of the patient can be combined to produce a three-dimensional image, or reconstruction, of the patient's internal anatomy. The two-dimensional images must be aligned, so that the most accurate correspondences between anatomic units are maintained. Digital images obtained directly from a CT or MRI scanner are automatically in register (provided no patient motion occurred during the scan), and may be used directly to create three-dimensional reconstructions. If the images are digitized from hard copy film records of a CT or MRI scans, the resulting digital images must be aligned to produce the needed correspondences. There exist several known methods for accomplishing this alignment, either by computational or interactive means.
A resulting three-dimensional reconstruction is a representation of the patient anatomy which contains abnormal tissue in a subset of the reconstruction. This subset may be referred to as volume of interest. This volume may be viewed in several ways. The simplest way is to view the original CT or MRI section images, or two-dimensional images which are derived from the original section images which present views of the volume of interest in planes that are orthogonal or oblique to the original section images. Alternatively, two-dimensional image data from multiple sections can be combined to form three-dimensional representations in which the anatomic units are depicted as solid opaque or translucent objects that may be interactively rotated, translated, and scaled using conventional computer graphics viewing program.
The three-dimensional reconstruction of the volume of interest containing diseased tissues or abnormal organs is created so that the spatial shapes, locations, and arrangements of organs may be used to prepare a three-dimensional radiation therapy treatment plan ("RTTP"). To develop the RTTP, the organ shape and arrangement information may be obtained from the three-dimensional reconstruction. Specifically, organ and tumor boundaries may be recorded from the two-dimensional images comprising the three-dimensional reconstruction. The two-dimensional images may be viewed on a computer graphics display and the edges of the organs and other relevant objects in the images may be recorded by manually tracing the edges in the displayed graphic using graphic control devices, typically a mouse and screen cursor combination. Resolution within the original two-dimensional section images is related to the pixel-to-pixel distance determined by the scanner settings, and the resolution in the third dimension is related to the section-to-section distance.
Three-dimensional reconstructions may be displayed in several formats. One such format is shown in FIG. 1A. FIG. 1A shows head surface 112, which is constructed from traverse CT sections. After points or areas of interest, or anatomical boundaries, are selected in two-dimensional images, a three-dimensional reconstruction containing these boundaries may be formed for the volume of interest containing the site or sites of disease. An example of cancerous mass 104 in the head 112 is shown in FIG. 1B. The radiation treatment plan objective is to irradiate the volume of diseased tissue with the appropriate dose and, as much as possible, not affect surrounding healthy tissues.
Additional three-dimensional reconstructions of the cancerous mass created from CT or MRI scan during or after radiation therapy, can indicate whether the treatment is having the desired effect of reducing the size of the cancerous mass. Moreover, the additional three-dimensional reconstruction of the cancerous mass at its location on or in an organ also may be used to determine whether surgical techniques have been successful in removing the cancerous mass. If it has not been totally removed, the three-dimensional reconstruction will show what remains so that further treatment may be planned.
A RTTP is a collection of data that is used to calculate radiation dosages for a cancerous mass. RTTP includes anatomical data about the patient being treated and defines the volume, area, or point where radiation doses are to be calculated.
The anatomical data about the patient being treated may be derived from pixel images and contours. Pixel images, in this context, are data files corresponding to CT or MRI images that contain an array of numeric values which represent the image intensifies of the patient in a defined plane, i.e., transverse, sagittal, or coronal. These values may be used to reconstruct the image for display on a computer display. A pixel image is shown in FIG. 2, generally at 150.
Contours are connected line segments which define the outlines of individual anatomic or structural elements in a particular plane. These elements may include a patient outline, diseased tissue outline, and individual organ outlines, to name a few. A set of contours is shown in FIG. 3, generally at 175. Contours may be created using a manual digitizer to trace drawings from graph paper, a radiograph, or a hard copy of a CT or MRI image. Contours also may be created using a graphics control device such as a mouse to either trace a pixel image directly from a screen display or to define an area of the image for autocontouring.
Contours created by either of the methods just described are cross-sections of the boundaries of the anatomic units that may be used to create a three-dimensional reconstruction of the volume of interest displaying the spatial relationships among the anatomic units. It is necessary to make a series of these cross-sections that extend the length of the cancerous mass or target area, so that the entire tumor and all relevant neighboring anatomic units may be displayed.
The set of cross-sections associated with the cancerous mass or target volume may be referred to as data pools. Data pools may be further divided into study sets. The study sets are sets of cross-sections or information from specific cross-sections that are intended to be used with respect to a specific RTTP. A study set may be an entire data pool or just some elements of a data pool. The relationship between data pools and study sets is shown in FIG. 4, generally at 200.
Referring to FIG. 4, data pool 202 includes ten cross-sections. Shown below data pool 202 are three study sets. These are Study Set A 204, Study Set B 206, and Study Set C 208. Both Study Set A 204 and Study Set B 206 include cross-sections that are part of data pool 202. On the other hand, Study Set C 208 includes the entire data pool.
In the production of the cross-sections for the data pools and study sets, the body section of interest, such as head, neck, or prostate, define an anatomical site and the internal structures or areas of interest for which contours are constructed are anatomical structures. Anatomical structures include the heart, lungs (left and right), and the spinal cord, etc.
The calculation region is the region in the patient's anatomy where the radiation dose will be calculated under the RTTP, and is a subset of the volume of interest in the three-dimensional reconstruction. All of the points located in the region are used during dose calculation. Referring to FIG. 5, generally at 225, calculation volume 226 is indicated for head 228. The calculations will be made for volume 226. Conventional methods are used for making the actual dose calculations which will be applied in carrying out the RTTP.
Before the RTTP can be computed, the spatial locations and distributions of the diseased tissues and the neighboring normal anatomic structures must be defined using the traced contours. The treatment plan is designed so that the tumor and a surrounding volume are maximally and evenly irradiated while restricting, as much as possible, irradiation of neighboring sensitive tissues most likely to be damaged by radiation.
The development of accurate contours for the cross-sections of anatomic structures and diseased tissues is the most time-consuming aspect of the preparation of a RTTP. In most cases, the three-dimensional reconstruction is formed from a multi-planar CT or MRI study. Generally, the contours for each of the areas of interest must be traced from CT or MRI hard copy images or digital CT or MRI slice images. The contours that result define the volumes and shapes of the organs and diseased masses in the volume of interest. The time to perform this labor intensive task increases sharply with an increase in resolution corresponding to an increased number of cross-sections used to form the three-dimensional reconstruction. Contour preparation typically may take two to three hours and in some cases substantially more than this amount of time.
Segmentation is the subdividing of an image into discrete regions. In the present situation, it is subdividing an image into the anatomical units required to prepare a RTTP. Since an object can be equivalently described by its boundary or a map of its regional extent in the image, contouring, and segmentation may be complementary operations.
A number of autosegmentation methods have been proposed, and they can be divided into two main types: global and local. The global type seeks to contour all the organs/tumors/anatomical units in a CT or MRI image simultaneously in one computational operation. Such methods frequently use neural networks to implement clustering algorithms on the statistical feature space of an image. Two reviews of global segmentation methods in CT/MRI imaging are: Bezdek, J. C., Hall L. O., and Clarke L. P., "Review of MR image segmentation techniques using pattern recognition," Medical Physics, 20:1033-1048, 1993, and Vaidyanathan, M., Clarke, L. P., Velthuizen, R. P., et al., "Comparison of supervised MRI segmentation methods for tumor volume determination during therapy," Magnetic Resonance Imaging, 13:719-728, 1995. Applications of neural networks implementing fuzzy clustering algorithms in such applications has been reported in the following document: Bezdek, J. C. and Pal, S. K., Fuzzy Models for Pattern Recognition, IEEE Press, Piscataway, N.J., 1992. What is gleaned from these articles is that there is not a method that is capable of accurate segmentation of many image types. This is because different organs in many cases have similar gray-scale level property distributions, and high variances of these properties within organs. These particular problems pose very significant difficulties, and have prevented the wider use of automated image segmentation in medical imaging.
Now turning to the local type, local autosegmentation seeks to contour organs or lesions singly, based on some user-input information. Systems have been reported which purport to accurately segment the brain from the CT or MRI scans of the head. An example is reported in Cline, H. E., Lorensen, W. E., Kikinis, R., and Jolesz, F., "Three-dimensional segmentation of MR images of the head using probability and connectivity," Journal of Computer Assisted Tomography, 14:1037-1045, 1990. However, no local type has been reported that works successfully on a range of tissues from many different parts of the body.
It would be highly desirable to have an autosegmentation/autocontouring method that would significantly reduce the amount of time to accomplish accurate segmentation and not have the problems of prior autosegmentation/autocontouring systems and methods.